Distributed Lazy Q-learning for Cooperative Mobile Robots, Page 05-13
Claude F. Touzet, University of Provence, Integrative and Adaptative Neurobiology Laboratory, St Jerome Faculty, Marseille, France

Abstract: Compared to single robot learning, cooperative learning adds the challenge of a much larger search space (combined individual search spaces), awareness of other team members, and also the synthesis of the individual behaviors with respect to the task given to the group. Over the years, reinforcement learning has emerged as the main learning approach in autonomous robotics, and lazy learning has become the leading bias, allowing the reduction of the time required by an experiment to the time needed to test the learned behavior performance. These two approaches have been combined together in what is now called lazy Q-learning, a very efficient single robot learning paradigm. We propose a derivation of this learning to team of robots : the «pessimistic» algorithm able to compute for each team member a lower bound of the utility of executing an action in a given situation. We use the cooperative multi-robot observation of multiple moving targets (CMOMMT) application as an illustrative example, and study the efficiency of the Pessimistic Algorithm in its task of inducing learning of cooperation.
Keywords: Cooperative robotics, cooperative learning, CMOMMT, Q-learning, lazy learning, reinforcement learning, heterogeneous robots

Neural Networks in Mobile Robot Motion, Page 15-22
Danica Janglova, Institute of Informatics SAS, Bratislava, Slovak Republic

Abstract: This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.
Keywords: Mobile Robot, Neural Network, Ultrasound Range Finder, Path Planning, Navigation

Gyroscopically Stabilized Robot: Balance and Tracking, Page 23-32
Yongsheng Ou & Yangsheng Xu, Department of Automation and Computer-Aided Engineering, The Chinese University of Hong Kong, China

Dynamic Replanning in uncertain environments for a sewer inspection robot Robotic Applications in Cardiac Surgery, Page 33-38
Oliver Adria; Hermann Streich & Joachim Hertzberg, Fraunhofer Institute for Autonomous Intelligent Systems, Sankt Augustin, Germany

Abstract: The sewer inspection robot MAKRO is an autonomous multi-segment robot with worm-like shape driven by wheels. It is currently under development in the project MAKRO-PLUS. The robot has to navigate autonomously within sewer systems. Its first tasks will be to take water probes, analyze it onboard, and measure positions of manholes and pipes to detect polluted-loaded sewage and to improve current maps of sewer systems. One of the challenging problems is the controller software, which should enable the robot to navigate in the sewer system and perform the inspection tasks autonomously, not inflicting any self-damage.
This paper focuses on the route planning and replanning aspect of the robot. The robot’s software has four different levels, of which the planning system is the highest level, and the remaining three are controller levels each with a different degree of abstraction. The planner coordinates the sequence of actions that are to be successively executed by the robot.
Keywords : autonomous, sewer inspection, AI planning, software

WebotsTM: Professional Mobile Robot Simulation, Page 39-42
Olivier Michel, Cyberbotics Ltd., BIRG & SWIS research groups, Swiss Federal Institute of Technology, Lausanne, Swiss

Abstract: Cyberbotics Ltd. develops WebotsTM, a mobile robotics simulation software that provides you with a rapid prototyping environment for modelling, programming and simulating mobile robots. The provided robot libraries enable you to transfer your control programs to several commercially available real mobile robots. WebotsTM lets you define and modify a complete mobile robotics setup, even several different robots sharing the same environment. For each object, you can define a number of properties, such as shape, color, texture, mass, friction, etc. You can equip each robot with a large number of available sensors and actuators. You can program these robots using your favorite development environment, simulate them and optionally transfer the resulting programs onto your real robots. WebotsTM has been developed in collaboration with the Swiss Federal Institute of Technology in Lausanne, thoroughly tested, well documented and continuously maintained for over 7 years. It is now the main commercial product available from Cyberbotics Ltd.
Keywords: WebotsTM, mobile robot simulation, rapid prototyping, transfer to real robots, commercial software